Behavioral content discovery

ABSTRACT

A system and a method for automatically collecting content, the method comprising the steps of: defining a plurality of content sites, creating a collection of virtual agents data including user characteristic data and user behavioral data, presenting the collection of virtual agents to the plurality of content sites; receiving content from the visited internet site; and storing the received content or presenting it to a user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional application being filed under 37CFR 1.53(b) and 35 USC 111, claiming the benefit of the priority date ofthe U.S. Provisional Application for patent that was filed on Nov. 13,2013 and assigned Ser. No. 61/903,410, which is hereby incorporated byreference.

FIELD

The method and apparatus disclosed herein are related to the field ofsurveying and measuring Internet advertisement effectiveness andefficiency and, more particularly, but not exclusively, to emulating auser behavior and characteristics while performing Internetadvertisement survey.

BACKGROUND

Personalized advertisement in the Internet is well known in the art.Personalized advertisement adapts the advertisement presented to a uservisiting a web page according to the characteristics of the particularuser. Therefore, when surfing the Internet and visiting various webpages, different users are presented with different advertisements. Thevisited website should therefore identify the characteristics of eachuser visiting the website. There are many methods for online tracking ofa user, studying the user's online behavior, analyzing the user'scharacteristics, and presenting the relevant information to the website.These methods are evolving continuously, affecting the offering ofadvertisements to the visiting user and the user's online experience.

Internet advertisement surveys study the offering of advertisementspresented to various users. The advertisement survey enables theadvertiser to understand how a user experiences Internet advertising andthe impact of a particular ad in view of the overall offering ofadvertisements. An advertisement survey should therefore focus on therelative impact of a particular advertisement on a particular type ofuser. However, Internet advertisement surveys do not enable thesurveying advertiser to adapt the survey to the particularcharacteristics of the audience to which a particular advertisement istargeted. There is thus a need in the art for, and it would be highlyadvantageous to have, a method and a system for Internet advertisementsurveys devoid of the above limitations.

SUMMARY OF EXEMPLARY EMBODIMENTS

According to one exemplary embodiment there is provided a method, asoftware program, and a system, for collecting content, the methodincluding the steps of: receiving from a first user a content location,the Internet location being one or more of an Internet address, anInternet link, and a universal resource locator (URL), a user locationand user data. The user location may include the geographical locationof a second user. The user data may include one or more characteristicof the second user as well as behavioral data of the second user. Themethod includes accessing from an Internet server, a destinationInternet site according to one or more of the content location and userlocation, receiving content from the destination internet site, andpresenting the content to the first user, and/or storing the content.The step of accessing from an Internet server a destination Internetsite uses the content location, an Internet Protocol (IP) addressrepresenting the user location, and the user data.

According to another exemplary embodiment, there is provided a method, asoftware program, and a system, for collecting content. The methodincludes the steps of: creating a content collection including aplurality of content locations where each of the content locationsincludes one or more of an Internet address, an Internet link, and auniversal resource locator (URL). The method includes creating a usercollection including a plurality of virtual agents data where eachvirtual agent data includes one or more of the user characteristic dataand user behavioral data in a data structure comprehensible by thecontent site. The method also includes automatically accessing, by anInternet server using the virtual agent data, a content location in thecontent collection, receiving content from the destination internetsite, repeating the above steps, and presenting the content to a user,and/or storing the content. These steps may be repeated for a pluralityof content locations and a plurality of virtual agent data.

According to yet another exemplary embodiment, there is provided amethod, a software program, and a system, for collecting content. Inthis embodiment, the method includes the step of receiving a contentlocation, wherein the content location may be any one of an Internetaddress, an Internet link, and a universal resource locator (URL). Themethod also includes receiving a plurality of user data, where the userdata includes one or more characteristic of the second user and/orbehavioral data of the second user. The method continues by accessingthe content location from an Internet server, retrieving at least oneadvertisement from the content location, and presenting the content tothe first user, and/or storing the content. The step of accessing thecontent location is repeated, each time using a different user data fromthe plurality of user data.

According to still another exemplary embodiment, there is provided amethod, a software program, and a system, for collecting content byadditionally including the steps of receiving from a user at least onevalue for at least one parameter, and presenting the content to theaccording to the parameters.

Further, according to another exemplary embodiment, there is provided amethod, a software program, and a system, for collecting contentadditionally including the steps of receiving from a user at least oneuser-characterizing content-location, and accessing theuser-characterizing content-locations before the step of accessing thecontent location.

Still further, according to another exemplary embodiment, there isprovided a method, a software program, and a system, for collectingcontent. In this embodiment, the method additionally includes the stepsof receiving from a user at least one referencing content-location, andaccessing the referencing content-location before the step of accessingthe content location.

Yet further, according to another exemplary embodiment, there isprovided a method, a software program, and a system, for collectingcontent. In this embodiment, the method additionally includes the stepsof receiving from a user at least one keyword, receiving from a user atleast one referencing content-location, accessing the referencingcontent-location, and presenting the keyword to the referencingcontent-location, where the steps of accessing the referencingcontent-location and presenting the keyword to the referencingcontent-location are performed before the step of accessing the contentlocation.

Even further, according to another exemplary embodiment, there isprovided a method, a software program, and a system, for collectingcontent. In this embodiment, the method additionally includes the stepsof receiving from a user at least one user-characterizing IP-address,and accessing the content location via the user-characterizingIP-address.

Additionally, according to another exemplary embodiment, there isprovided a method, a software program, and a system, for collectingcontent. In this embodiment, the method includes the steps of receivingfrom a user one or more user-characterizing parameters, and one or moreadvertisement-characterizing parameters, presenting to the user one ormore advertisement-characterizing parameters associated with theuser-characterizing parameters, and user-characterizing parametersassociated with the advertisement-characterizing parameters.

According to still another exemplary embodiment, there is provided amethod, a software program, and a system, for collecting content. Inthis embodiment, the method includes the steps of receiving from a userone or more user-characterizing parameters andadvertisement-characterizing parameters, presenting to the user one ormore statistical parameters calculated for at least oneadvertisement-characterizing parameter associated with theuser-characterizing parameter, and/or statistical parameter calculatedfor at least one user-characterizing parameter associated with theadvertisement-characterizing parameter.

According to another exemplary embodiment, there is provided a method, asoftware program, and a system, for optimizing a survey ofadvertisements. In this embodiment, the method includes the steps ofreceiving a plurality of content locations and a plurality of user data,where each pair of content location and user data includes a cell. Themethod continues by accessing the content location from an Internetserver and presenting the user data to the content location. Further,the method retrieves at least one advertisement from the contentlocation forming a result, repeating the above-listed steps for a firstplurality of cells. The method then compares the results of the firstplurality of cells forming a distribution of comparisons, selecting asecond plurality of cells according to a distribution of the comparisonsof cells, and repeating previous steps for the selected cells.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe relevant art. The materials, methods, and examples provided hereinare illustrative only and are not intended to be limiting. Except to theextent necessary or inherent in the processes themselves, no particularorder to steps or stages of methods and processes described in thisdisclosure, including the figures, is intended or implied. In many casesthe order of process steps may vary without changing the purpose oreffect of the methods described.

Implementations of the methods and systems of the various embodimentsdescribed herein involve performing or completing certain selected tasksor steps manually, automatically, or using any combination thereof.Moreover, according to actual instrumentation and equipment of thevarious embodiments of the method and system described herein, severalselected steps could be implemented by hardware or by software on anyoperating system of any firmware or any combination thereof. Forexample, as hardware, selected steps of embodiments described hereincould be implemented as a chip or a circuit. As software, selected stepsof embodiments described herein could be implemented as a plurality ofsoftware instructions being executed by a computer using any suitableoperating system. In any case, selected steps of the method and systemof the embodiments described herein could be described as beingperformed by a data processor, such as a computing platform forexecuting a plurality of instructions.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments are described herein, by way of example only, withreference to the accompanying drawings. With specific reference now tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of theembodiments only, and are presented in order to provide what is believedto be the most useful and readily understood description of theprinciples and conceptual aspects of the embodiments. In this regard, noattempt is made to show structural details of the embodiments in moredetail than is necessary for a fundamental understanding of the subjectmatter, the description taken with the drawings making apparent to thoseskilled in the art how several forms and structures may be embodied inpractice.

In the drawings:

FIG. 1 is a simplified illustration of an advertisement surveyenvironment including an advertisement survey system;

FIG. 2 is a simplified block diagram of software program foradvertisement survey system;

FIG. 3 is a simplified flow chart of process 1 implementing theadvertisement survey method;

FIG. 4 is a simplified flow diagram of a module for implementingBehavioral Content Discovery method by a survey process; and

FIG. 5 is a simplified flow diagram of a process for optimizing anadvertisement survey by merging and splitting survey parameters.

FIG. 6 is a simplified block diagram of hierarchical structures ofgeo-locations.

FIG. 7 is a simplified block diagram of hierarchical structures of sporttypes.

FIG. 8 is a simplified block diagram of hierarchical structures of ageranges, according to an exemplary embodiment.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

The principles and operation of a method and a system for surveyingInternet advertisements according to several exemplary embodiments maybe better understood with reference to the drawings and accompanyingdescription.

Before explaining at least one embodiment in detail, it is to beunderstood that the embodiments are not limited to the details ofconstruction and the arrangement of the components set forth in thefollowing description or illustrated in the drawings. Other embodimentsmay be practiced or carried out in various ways. Also, it is to beunderstood that the phraseology and terminology employed herein is forthe purpose of description and should not be regarded as limiting.

In this document, an element of a drawing that is not described withinthe scope of the drawing and is labeled with a numeral that has beendescribed in a previous drawing has the same use and description as inthe previous drawings. Similarly, an element that is identified in thetext by a numeral that does not appear in the drawing described by thetext, has the same use and description as in the previous drawings whereit was described.

The drawings in this document may not be to any scale. Differentdrawings may use different scales and different scales can be used evenwithin the same drawing, for example different scales for differentviews of the same object or different scales for the two adjacentobjects.

Various embodiments presented herein comprise a method and a system forInternet advertisement surveys, enabling the surveyor to adapt thesurvey to the characteristics of the target audience. Particularly, thevarious embodiments enable the surveyor to emulate one or more types ofusers and then study the advertising experience of these types of users.Particularly, the advertisement survey can be adapted to measure theimpact of a particular advertisement in view of the overall advertisingexperience of a particular type of user. A user type is generallycharacterized by the user's behavior while surfing the Internet, asaccumulated by the relevant Internet sites and other online mechanisms.

In this document, the terms Internet advertisement survey, Internetadvertising survey, advertisement survey, advertisements survey, andadvertising survey have the same meaning unless a different meaning isexplicitly stated. In this respect, the terms survey, research, study,examine, measure, or map may also be used interchangeably to denote theact of investigating the distribution of advertisements presented tousers according to predefined survey parameters. The term bid orbidding, especially when referring to advertisement bidding, also meansauction or auctioning, as well as, real-time bidding, online ad auction,and combinations thereof.

Reference is now made to FIG. 1, which is a simplified illustration ofan advertisement survey environment 10 including an advertisement surveysystem 11 according to an exemplary embodiment.

As seen in FIG. 1, the advertisement survey environment 10 may includethe following elements:

A public data communication network such as the Internet, the cloud,etc. 12;

A plurality of content servers 13 communicatively coupled to theInternet 12 and containing content 14, such as websites and web pages,and which may contain advertisement placeholders 15;

A plurality of advertising servers 16 communicatively coupled to theInternet 12 and typically containing various advertisements 17; and

Advertisement survey system 11, which may include a plurality of surveyservers 18 and one or more terminals or workstations 19, allcommunicatively coupled to the Internet 12 and typically operated by oneor more users 20, who function as the operators or administrators of theadvertising survey system 11.

It should be appreciated that the advertisement survey environment 10 asillustrated in FIG. 1 is simplified, and, for example, an advertisingserver 16 may represent a complex system of servers storing, bidding,exchanging and communicating bidding data and advertisements betweenadvertisers and visited web-pages. An advertising network is anorganization operating one or more advertising servers 16. It isappreciated that advertising servers 16 can be characterized andidentified according to their respective advertising network. Parametersused to characterize an advertising network include, but are not limitedto: company name, type of business such as ad-network, Agency, affiliatenetwork, redirecting URL, market category or segment (Vertical), etc.

Typically, when a user 21 accesses the Internet (referred to as surfingthe Internet or web), the user also accesses Internet content 14, hostedon content server 13, such as a website or a web page that includes theweb page including content 14 and an advertisement placeholder 15. Thecontent 14, the advertisement placeholder 15, or the content server 13contacts an advertising server 16 that is operative to place anadvertisement 17 into the placeholder 15. Typically, the content server13 hosting the content 14 and/or advertisement placeholder 15 alsoprovides the advertising server 16 with some user information 21pertaining to the particular user currently accessing the particularcontent 14. Such user information may be derived from various sourcessuch as:

The Internet address (IP address) of the user's computing device 22.

Surf tracking data, such as the identity of a previous website visitedby the user, and/or referring the user to the current website or webpageas well as the navigational actions of the user at that website.

Terms of user's interests and similar data, such as search terms,typically collected by the current website.

User data 23.

User data 23 may be a cookie or a similar piece of data, typicallystored in the user's computing device 22. The computing device 22 may beany device or system used by the user to surf the Internet and to accessInternet content. As such, the computing device may include a cellulartelephone (a smartphone), a tablet, a laptop computer, or a personalcomputer as a few non-limiting examples. User data 23 may include datathat characterizes the user, as well as the hardware and software usedby the user's computing device 22. Therefore, typically, when a user 21accesses a web-based content 14, the user's computing device, via theuser data 23 and Internet address, has an affect or impact on theadvertisements 17, as well as the form and look-and-feel of theadvertisements 17 that are presented to the user 21. Two users accessthe same website may thus have a different online experience and, thesame user may even have a different online experience when visiting thesame website using a different computing devices 22, such as when usingsomeone else's computing device.

When performing advertisement surveys, the advertisement survey system11 may access content 14 located on a content server 13 as a virtualagent. The virtual agent emulates a user, such as user 21, typically bypresenting the content 14 website with virtual agent data 24, typicallyemulating the user data 23. For example, when performing anadvertisement survey, the advertisement survey system 11 accesses aplurality of content 14 websites, typically by using a plurality ofdifferent virtual agent data 24. The advertisement survey system 11 maypresent the accessed content 14 websites with a plurality of virtualagent Internet Protocol addresses (also known as Internet addresses, IPaddress, universal resource locator (URL), hyperlink, Internet link, orsimply link). The virtual agent IP address may be the IP address of theparticular survey server 18 accessing particular content server 13containing a particular content 14.

It is appreciated that a particular content 14 may be provided byseveral content servers 13 and that accessing the particular content 14from a different survey server 18, such as servers located in differentgeographical locations, may result in accessing a different contentserver 13, and thereafter contacting a different advertisement server 16and receiving a different advertisement 17.

It is appreciated that different content servers 13 collect user datasuch as surf tracking data and terms of interest in different manners,methods, and/or mechanisms, and therefore providing different userinformation 21 to the respective advertising servers 16. Thus, to surveythe advertising experience of a particular type of user, the surveyserver 18 should properly emulate the characteristics of the typicaluser as collected by the various content servers 13 and provided to thevarious advertising servers 16 in the form of user information 21. Hencethe survey server 18 surfs the Internet in a particular, pre-plannedorder and/or manner, to provide the content servers 13 with thepre-planned particular data forming a user information package 22adequately representing the typical user 21 whose advertising experienceis surveyed.

It is also appreciated that a publisher may operate several contentservers 13, websites, web pages, and content 14. Thus, content servers13 and content 14 can be organized, characterized, identified andaccessed (surveyed) according to their respective publishers. Apublisher is typically characterized by company or brand name, marketcategory or segment (vertical), related geography, etc.

Hereinafter, unless explicitly explained, the term virtual agent data 24denotes all data characterizing the virtual agent, including theInternet address, user specific data collected by the visited website,(such as surf tracking data and terms of interest), and user data 23.

After presenting the virtual agent data 24 to the visited content server13, the advertisement survey system 11, via its survey server 18,retrieves, stores and presents to a user the advertisements 17 presentedto the virtual agent by the content server 13 within the advertisementplaceholders 15 at the content 14 presented by the content server 13.

The advertisement survey system 11 also analyses the informationretrieved from content servers 13 (the advertisements 17) with respectto the information presented to the content servers 13 (virtual agentdata 24) to produce statistical advertising data and parameters. Suchstatistical advertising data may include measurements of the relativeimpact of a particular advertisement, or advertisement type on aparticular visitor, as characterized by a particular virtual agent data24. Advertisement impact may also include the frequency of the variousadvertisements, the relative frequencies, the share of voice, etc., allwith respect to a particular type of visitor (as characterized by therespective virtual agent data 24).

Reference is now made to FIG. 2, which is a simplified block diagram ofsoftware program 25 for advertisement survey system 11 according to anexemplary embodiment.

As seen in FIG. 2, software program 25 may include the followingcomponents:

A human interface module 26.

A survey module 27.

An analyzing and reporting module 28.

A storage or database module 29.

Human interface module 26 may enable a user to set the parameters of anadvertisement survey as described above, and further detailed below,typically using survey administration module 30. The parameters of anadvertisement survey may include, but are not limited to, the followingparameter groups: targeted user characteristics 31, targeted contentwebsites 32, targeted advertisement characteristics 33, and targetedcompetitive advertisement data 34 and/or targeted placeholders 15.

Human interface module 26 enables a user to set targeted usercharacteristics 31 using survey administration module 35. Targeted usercharacteristics 31 may include, but are not limited to, technicalparameters, surfing history, personal parameters, and survey parameters.Technical parameters include, but are not limited to, geographicallocality (Geo-location) such as country, region, town, etc. of the user(user location), user or browser language, browser type (make, version,etc.), operating system type, screen size, access network speed (bitrate), etc. Surfing history includes, but is not limited to, referrerwebsite, referrer keywords, etc. Referrer website is typically the site(or sites) visited prior to accessing the targeted content 14. Referrerkeywords are the terms used by a search engine to locate a website orwebpage (including referrer websites).

Personal data (or personal parameters, or user category) include, but isnot limited to, gender, age or age range, level of education, income,salary or any other socio-economical characterization, fields ofinterests, and any other data associated with the content of websitesand web pages searched or visited by the user in the past. Such data maybe collected in cookies placed by the visited websites and thenpresented to advertising bidding engines. Personal data may be createdby the virtual agent by visiting adequate websites and/or web pagesprior to accessing the targeted content 14 website. Therefore, using thehuman interface module 26 to set the personal data may include setting alist of user-characterizing content-locations 36, which are websites(e.g. URLs) that the virtual agent should visit prior to accessing thecontent 14 web page.

Typically, a content 14 website collects the targeted usercharacteristics 31 (including technical parameters and the surfinghistory) from the accessing browser. The survey server 18 typicallyemulates the behavior of a normal browser and provides this data to thecontent 14 website in a form such as virtual agent data 24.

Targeted user characteristics 31 may also be named, or include, userbehavioral data. Targeted user characteristics 31 are used tocharacterize and create virtual agents by providing virtual agent datasuch as virtual agent data 24 of FIG. 1.

Targeted user characteristics 31 may also include session length.Session length is typically the time the user spends visiting aparticular website or webpage, as measured by the website. Someadvertising methods may change the advertisement if the session lengthexceeds a certain threshold, such as 5 minutes.

Human interface module 26 also enables a user to set the parameters oftargeted content websites 32 (destination Internet sites or pages) usingwebsite targeting module 37. Targeted content websites 32 refer to, butare not limited to, a list of content websites that should be visitedand surveyed, or the characteristics of such targeted content websites.Such targeted content websites or web pages refer to content 14 hostedby content servers 13 of FIG. 1.

Targeted content websites 32 may be characterized and/or identified bytheir Domain (URL), a website title, and category or vertical (such as avertical market). The targeted content websites 32 may be provided as alist of content locations such as Internet addresses of websites and/orweb pages. Internet links to websites and/or web pages, and a universalresource locators (URL) of websites and/or web pages.

Alternatively, targeted content websites 32 can be selected orcharacterized according to the publisher or a characteristic of thepublisher, such as company or brand name, market category or segment(vertical), related geography, etc.

Human interface module 26 also enables a user to set the parameters oftargeted advertisement characteristics 33 using advertisement targetingmodule 38. Targeted advertisement characteristics 33 include, but arenot limited to, an advertisement, or a plurality of advertisements,which impact should be measured. Typically, the targeted advertisementis published by the business entity that ordered the advertisementsurvey. Typically, such business entity is interested in measuring theimpact of the targeted advertisement in relation to other advertisementspresented to the targeted users.

Human interface module 26 also enables a user to set the parameters oftargeted competitive advertisement 34 using competitive targeting module39. Targeted competitive advertisement 34 data refers to data includedin an advertisement presented to the user and characterizingadvertisements other than the targeted advertisements. Targetedcompetitive advertisement 34 data may enable the advertisement surveysystem 11 to locate and identify each advertisement presented, and/orthe advertiser, that is the entity that pays for the presentation of theadvertisement to the user visiting the website. An advertiser may becharacterized (for searching advertisements) and or identified (in acollected advertisement) according to parameters such as name and type,such as company name, brand name, market type, etc.

Targeted competitive advertisement 34 data may enable the advertisementsurvey system 11 to locate and identify other characteristics of theadvertisement, such as technical parameters (i.e., object type, physicalsize and/or dimensions, digital size such as file size, and name). Anobject type typically identifies the medium used by the advertisementsuch as image, flash object, or JS object running rich media.

Targeted competitive advertisement 34 data may enable the advertisementsurvey system 11 to characterize an advertisement according to placementgroup. In a particular web page some advertisements can be related to acertain group of advertisements. Such a group can be characterized, forexample, according to an advertising campaign. Typically, an advertisertakes over a particular advertising space and presents a group ofadvertisements related to the same campaign. Such takeovers are usuallypriced as premium channels by the publishers. Typically, an advertisingcampaign includes one or more advertisements leading to the same uniquelanding page. An advertising campaign may be characterized andidentified by its name, category or market segment (vertical), a uniquelanding URL, a campaign title typically appearing in the landing page, acampaign group, etc.

Alternatively, there is no differentiation between targetedadvertisements and competitive advertisement in the research phase, andthe differentiation is made in the analysis and reporting phase. In thisalternative, the targeted advertisement characteristics 33 module andthe targeted competitive advertisement 34 module are (also) available inthe analyzing and reporting module 28.

Human interface module 26 may also enable a user to set the parametersof the scope of an advertisement survey, typically using the surveyadministration module 30. The advertisement survey scope 40 refers tosurvey parameters such as, but not limited to:

The number of virtual agents and/or virtual agent types, such as virtualagent data 24 of FIG. 1, to be created.

The number of targeted content websites 32 to be visited.

Day and time in which the surveying virtual user should access thetargeted content 14 website. This is usually provided by setting a listof days of the week, and/or a span of hours of the day, where thevirtual agent should access the targeted content 14 website.

User characteristics such as geo-location and language.

Hardware and software characteristics associated with the user such asbrowser language, browser type, operating system type, screen size orresolutions, bandwidth of the user's access network, etc.

Referrer websites and keywords used by the user.

The required survey accuracy, typically referring to the accuracy of thestatistical result, such as ad saturation value derived using adsaturation analysis, daisy chain analysis, etc.

Merging and splitting survey parameters.

Using report management module 41, the human interface module 26 mayalso enable a user to set the parameters of an advertisement surveyreport. The human interface module 26 may enable a user to select themeasured parameters and the analyzed parameters to present to a user inan advertisement survey report, and the presentation manner and format.

As described above, an advertisement survey may have many parameters andthe advertisement survey system 11 is expected to collect advertisementdata for all the relevant combinations of the survey parameters. A cellmay be a combination of search parameters. It is therefore useful tooptimize the advertisement survey by using useful parameters. Usefulparameters create cells providing different results. For example, twocells that provide the same results can be merged without loss of usefulinformation. Similarly, if two cells provide very different results itmay be useful to split the cells, increase the resolution of the survey,and thus increase the accuracy of the results. Merging and splittingsurvey parameters is therefore a method of survey optimization. Most ofthe survey parameters can be merged and split. For example, thegeographical resolution (geo-location), the span of keywords, the numberof time divisions (time-of-day spans) and most of all the number andvariability of types of virtual agents.

As an option, an advertisement survey can be re-run repeatedly. Anadvertisement survey may be scheduled for periodical re-run, for exampledaily, or weekly, or monthly. A subsequent re-run can be optimized basedon the results of the previous run. This means that some of the cellsare merged and other cells are split. For example, assume a surveydefined for 24 hours divided into six periods of 4 hours. Assume twoperiods provide the same results. These two periods (cells) can bemerged. On the other hand, assume two periods provide results that arevery different. These two periods (cells) can be split, for example,into four periods of two hours each.

Human interface module 26 may enable a user to set the parameters formerging and splitting survey parameters (cells) by setting rules andcriteria for merging, splitting and resetting. For example, a mergingrule uses the difference between particular results of two adjacentcells and the criterion is a difference value or percentage. If thedifference is below the criterion the two cells are merged. For example,a splitting rule uses the difference between particular results of twoadjacent cells and the criterion is a difference value or percentage. Ifthe difference is above the criterion one or two of the cells are split.For example, after a predetermined period, or number of runs, mergingand splitting are rest to the original setup. It should be appreciatedthat a level of hysteresis may also be employed by setting a lowercriterion threshold for determining a merging event and an uppercriterion threshold for determining a splitting event, such that thearea between the two thresholds does not result in any action.

Alternatively, advertisement survey system 11 performs surveyoptimization, such as merging and splitting, on the fly, while a surveyis being executed. As the results of the processes are collected andanalyzed, survey parameters can be merged and split thus generating moreor less processes and modifying the survey parameters assigned to theseprocesses.

Human interface module 26 enables a user to define an optimizationmechanism whereby advertisement survey system 11 optimizes anadvertisement survey on-the-fly, or optimizes a repeated (next)advertisement survey when an advertisement survey is scheduled forperiodical re-run. A user may use optimization setup module 42 to createor modify a set of optimization parameters 43. Optimization parameters43 may include, but are not limited to, optimization algorithms, rules,criteria, and tools.

Algorithms refer to methods of analyzing and calculating results bywhich optimization is considered.

Rules refer to mechanisms for determining an event requiringoptimization. For example:

Rules that determine which cells to compare (e.g. rules for selectingadjacent cells).

Rules that determine which algorithm to use to calculate the values tocompare.

Rules determining the comparison method to use for determining a needfor optimization. For example, by measuring a difference between resultscalculated by a particular algorithm for two cells of a particular type.

Criteria refer to the value, such as a threshold value, by whichadvertisement survey system 11 determines that optimization is requiredfor a particular cell (e.g. split) or a plurality of cells (e.g. merge).

Tools refer to the method for applying optimization, such as mergingcells, splitting cells, cell organization (e.g. hierarchical structureof cells), and rest conditions (i.e. when to reset the optimization andreturn to the original cell structure).

Survey module 27 may include:

Survey management module 44.

IP Hopping module 45.

Behavioral Content Discovery module 46.

Advertisements identification module 47.

Ad Saturation Analysis module 48.

Daisy Chain Analysis module 49.

The survey management module 44 manages a plurality of processes (task),where each process emulates a virtual agent. The processes are typicallyexecuted by one or more servers. A process may execute any number and/orcombination of the modules of the survey module 27. A process mayexecute (perform) the Behavioral Content Discovery module 46.

The survey management module 44 uses data from the advertisement surveyscope 40 to determine how many processes are necessary and how to managethe distribution of virtual agents among the processes. The surveymanagement module 44 assigns to each process the relevant data toexecute the Behavioral Content Discovery module 46 for a particularvirtual agent. Such data may include data derived from the targeted usercharacteristics 31, the targeted content websites 32, the targetedadvertisement characteristics 33, and the targeted competitiveadvertisement 34.

The IP hopping module 45 may be used by the survey management module 44or by each of the surveying processes to determine the IP address to usewhen accessing a particular content server 13. The IP hopping module 45selects the appropriate IP address according to the geographicallocality (Geo-location) required to emulate the virtual agent, avoidingthe use of the same IP address for virtual agents accessing the samecontent server 13, whether using the same or different virtual agentdata 24.

The Behavioral Content Discovery module 46 produces the required virtualagent data 24 of the virtual agent and presents it to the content server13. The Behavioral Content Discovery module 46 generates cookies,presents relevant keywords to search engines, visits particularwebsites, etc. in order to produce the agent data 24 and then enablesthe visited content server 13 to collect this data.

Then, the Behavioral Content Discovery module 46 collects from thevisited content server 13 information regarding the visited placeholders15, and the advertisements 17 presented to the virtual user within thevisited placeholders 15. Information regarding the visited placeholders15 may include size, relative position (in the page), object type,placement group, and display properties (such as floating, hovering,aligned, etc.)

The surveying process may then execute the advertisements identificationmodule 47 to identify the various advertisements 17 collected from thevisited content server 13. Ad Saturation Analysis module 48 and DaisyChain Analysis module 49 are then used to further analyze the behaviorof the advertising system. All the data collected and analyzed by eachsurveying process may be stored in the survey results database 50 of thestorage or database module 29.

The analyzing and reporting module 28 includes a user interface module51 and a data analysis and presentation module 52. User interface module51 enables a user to enter research parameters 53 and view browsethrough the research results 54 presented by the data analysis andpresentation module 52. The data analysis and presentation module 52uses the research parameters 53 to create the research results 54.

Research parameters 53 may include one or more, or any combination of,parameters from the parameters set by the human interface module 26. Forexample; the research parameters 53 may include parameters of targeteduser characteristics 31, parameters of targeted content websites 32,parameters of targeted advertisement characteristics 33, and parametersof targeted competitive advertisement data 34. Such parameters mayinclude, but are not limited to, user behavioral data such asgeographical locality (Geo-location) such as country, region, town,etc., user or browser language, browser type (make, version, etc.),operating system type, screen size, referrer website, referrer keywords,gender, age or age range, level of education, income, salary or anyother socio-economical characterization, fields of interests, andsimilar data, day and time in which the surveying virtual user accessedthe targeted content 14 website.

The data analysis and presentation module 52 analyzes the survey resultsdatabase 50 according to the research parameters 53. Such analysis mayinclude generating statistical results. The research results 54generated by the data analysis and presentation module 52 may includethe frequency (number) of advertisements 17 presented to virtual usersaccording to the advertisement type or identification, and according toa particular virtual user characterization parameter or combinations ofparameters. The research results 54 may also include relativefrequencies (percentage) of advertisements 17 presented to virtual usersaccording to the advertisement type or identification, and according tovirtual user characterization parameters or combinations of parameters.For example, such relative frequency include share-of-voice of aparticular advertisement regarding a particular selection of usercharacteristics (including location, day-of-week, time-of-day, etc.).

It is therefore appreciated that the advertisement survey system 11surveys the advertisement survey environment 10 using a method ofbehavioral content discovery, to collect and present advertisement data(the discovered content) according to user-selected (referring to theresearching user) user behavioral characteristics (referring to thecontent visiting user).

The user behavioral characteristics refer to data selected or entered bythe researching user using the human interface module 26 (and presentedto the visited content server 13 as virtual agent data 24) and/or theuser interface module 51. The advertisement data (or the discoveredcontent) refers to advertisements 17 as collected by the advertisementsurvey system 11 and stored in survey results database 50 and/orpresented to the researching user as research results 54.

Reference is now made to FIG. 3, which is a simplified flow chart ofprocess 55 implementing the advertisement survey method according to anexemplary embodiment.

As seen in FIG. 3, process 55 may execute software program 25 and mayinclude the following actions:

Setting the survey parameters 56.

Executing the survey 57.

Analyzing and reporting survey results 58.

In action 56 of process 55, a researching user prepares advertisementsurvey system 11 to execute a survey of Internet advertisements. Theuser may access human interface module 26 to set up the surveyparameters. The user, executing process 55, may perform one or more ofthe following steps (details of these parameters are described above):

Setting or selecting a virtual user (step 59)

Setting or selecting target content such as content servers 13 and/orcontent 14 (step 60).

Setting or selecting target advertisements (step 61).

Setting or selecting competitive advertisements (step 62).

Setting or selecting the scope of the survey (step 63).

After completing the survey setup, the researching user instructsadvertisement survey system 11 to execute a survey (step 64), thusinvoking action 57.

In action 57, advertisement survey system 11 executes the survey inthree main parts (or modules) designated in FIG. 3 by numerals 65, 66,and 67. In part 65 the advertisement survey system 11 supervises aplurality of survey processes (tasks) 68. Each survey process 68 mayemulate a particular virtual agent accessing a content server 13 and/orcontent 14. The advertisement survey system 11 creates a survey process68 (step 69), and assigns appropriate survey parameters to the surveyprocess 68 (step 70). Steps 69 and 70 are repeated for all surveyprocesses 68 until all the survey parameters (such as virtual agents,content server 13 and/or content 14) are assigned to survey processes 68(step 71).

Part 66 of action 57 of process 55 may be executed by each surveyprocess 68. In Part 66, a survey process 68 executes at least one of thesteps of Behavioral Content Discovery method (step 72), the AdSaturation Analysis (step 73), and the Daisy Chain Analysis method (step74).

Part 66 of action 57 of process 55 is typically a survey optimizationprocess, typically executed by the supervising task executing part 65,or, alternatively, by a separate task. Part 67 optimizes the currentadvertisement survey (via 75) or the next survey (via 76) as necessaryand/or possible. Part 67 may optimize the current advertisement surveyby merging and splitting survey parameters (cells) as described above.

If the survey parameters are modified on-the-fly (via 75) part 65, andparticularly process 70, may create new tasks and assign them the new ormodified parameters to improve the accuracy of the survey results.On-the-fly optimization is particularly useful when parameters (cells)are split, increasing the resolution of the survey where necessary.Thus, a survey can start with relatively low resolution andautomatically increase the resolution on-the-fly, while being executed.The advertisement survey system 11 may therefore be capable ofautomatically and continuously adapting the survey resolution (cellsize) according to the survey results and the predetermined accuracy.

Process 55 may then proceed to action 58, where a researching userconducts the analysis of the data collected by the advertisement surveysystem 11 in step 57 according to the survey parameters and methodsselected in step 56, and then reviews the analysis results.

Executing action 58 of process 55 first enables the researching user toselect the analysis parameters (step 77). Within this step theresearching user may then select any combination of parameters such asthe parameters used for determining the advertisement survey in step 56.Such parameters may be:

User behavioral data including targeted user characteristics, targetedcontent websites, targeted advertisement characteristics, and targetedcompetitive advertisement data.

User behavioral data or targeted user characteristics may includetechnical parameters, surfing history, personal parameters, and surveyparameters.

Technical parameters may include geographical locality (Geo-location),type of the terminal used by the user, operating system type, screensize, browser type, user's or browser's language, etc.

Surfing history typically includes referrer website, referrer keywords,etc. gender, age or age range, level of education, income, salary or anyother socio-economical characterization, fields of interests, and anyother data associated with the content of websites and web pagessearched or visited by the user in the past.

The researching user may also select a time frame in whichadvertisements were recorded by the advertisement survey system 11 instep 57. The time frame may include time of day and/or day of the week,etc. in which the analyzed advertisements were recorded.

The researching user may also select a list or a type of targetedcontent websites from which advertisements recorded by the advertisementsurvey system 11 in step 57 should be analyzed in step 78.

The researching user may also select one or more targeted advertisementand one or more targeted competitive advertisement.

Action 58 may then proceed to step 78 to analyze the data in surveyresults database 50 according to the parameters selected in step 77, andeventually, in step 79, process 55 presents the results of the analysisto the researching user.

It should be appreciated that the results of the advertisement surveyinclude relations and/or associations between characterizationparameters such as described above with respect to the parameters a usermay set using human interface module 26, and/or the parameters describedwith respect to table 1 below.

It should also appreciated that the user using the advertisement surveysystem 11 to analyze and present the results of an advertisement surveymay select one or more of the characterizing parameters such as theparameters presented in Table 1, and request the advertisement surveysystem 11 to present results associated with one or more otherparameters of table 1. It is appreciated that such results associatingtwo or more parameters may be calculated and presented as statisticalparameters and/or distributions.

A user of advertisement survey system 11 may therefore, for example,select one or more user-characterizing parameters, and/or one or moreadvertisement-characterizing parameters and instruct the system topresent one or more advertisement-characterizing parameters associatedwith the user-characterizing parameters, or user-characterizingparameters associated with the advertisement-characterizing parameter.Alternatively the system may present one or more statistical parameterscalculated for the advertisement-characterizing parameters associatedwith the user-characterizing parameters, and/or one or more statisticalparameters calculated for the user-characterizing parameters associatedwith said the advertisement-characterizing parameters.

Reference is now made to FIG. 4, which is a simplified flow diagram of amodule for implementing Behavioral Content Discovery method (step 72) bya survey process 68, according to an exemplary embodiment.

A survey process 68 performing the Behavioral Content Discovery method(step 72) may start by loading the virtual user parameters assigned toit (step 80). Survey process 68 may then set the parameters of thebrowser, or browser emulation module it uses (step 81).

Survey process 68 may then proceed to select an IP address (or proxy)according to the geo-location assigned to the virtual user it isemulating (step 82). This IP address or proxy serve as auser-characterizing IP-address. When the visited website or webpagedetects this IP address it also identifies the geo-location associatedwith the IP-address and hence associates the virtual agent with thatgeo-location.

Survey process 68 may then proceed to develop the requiredcharacteristics of the virtual user it is emulating, typically byaccessing one or more referencing content-location such as websites andwebpages (step 83), and using selected search keywords, if applicable(step 84). Steps 83 and 84 may be repeated until the characterization ofthe emulated virtual agent is completed (step 85).

Survey process 68 may then proceed to access a selected content server13 and/or content 14 (step 86) as assigned to it in step 69. Surveyprocess 68 may then proceed to present the emulated virtual userinformation to the accessed content server 13 and/or content 14,typically using one or more data structures comprehensible by thetargeted content site (step 87).

Survey process 68 may then proceed to collect from the accessed contentserver 13 and/or content 14 the advertisement 17 presented to thevirtual user (step 88). Survey process 68 may then proceed to identifyadvertisement 17 (step 89) and store them in storage or database module29 (step 90).

Therefore, using the system and method described above, theadvertisement survey system 11 can research and analyze theadvertisement survey environment 10. Advertisement survey system 11 mayoperate a plurality of servers, accessing a plurality of content sites,using a plurality of virtual agent data, typically at least partiallyconcurrently.

Advertisement survey system 11 may enable a researching user to analyzethe impact of a particular advertisement with respect to any otherselection of advertisements, or advertisement types, and in regard ofany number of types, or characterizations, of visiting users. The impactof an advertisement on the visiting user may be measured as the numberof visiting users exposed to a particular advertisement relative to thetotal number of advertisements presented to a particular type ofvisiting user. The impact of a particular may be also measured withrespect to advertisements of a particular type or any othercharacterization.

Hence, the researching user can use the advertisement survey system 11to study the potential impact of a particular advertisement, oradvertising concept, if provided within the current state and offeringof the advertisement environment 10. The researching user can use theadvertisement survey system 11 to study the potential impact of suchproposed advertisement with respect to any selected type orcharacterization of a visiting user surfing the advertisementenvironment 10.

Furthermore, the researching user can use the advertisement surveysystem 11 to study possibilities for segmenting the population ofvisiting users to assess the potential advertising impact value for theproposed segmentation and to optimize it. The segmentation of thevisiting users refers to the selection, or grouping, of the users'characteristics. Such segmentation may include, but is not limited to,age, gender, geo-location, language, socio-economic characteristics,fields of interest, web surfing history, etc.

Moreover, the advertisement survey system 11 enables the researchinguser to execute an advertisements survey of the advertisementenvironment 10 according to a first set of parameters (i.e. surveyparameters provided by the researching user in step 56) and then toanalyze the survey results according to a second set of parameters (i.e.analysis parameters provided by the researching user in step 77).

Thereby, the advertisement survey system 11 analyses the manner in whichadvertising servers 16 operate. Particularly, the advertisement surveysystem 11 analyses the set of advertising rules by which advertisingservers 16 publish advertisement 17 to various users. Advertisementsurvey system 11 imitates a large amount of virtual users with differentprofiles and collect all the data these presented to these virtualusers. Thereby, the advertisement survey system 11 analyses the rulesconnecting the advertising rules with the various user profiles,typically by finding data patterns. Table 1 is an example of datapresented by the advertisement survey system 11 to a content server 13,and data collected by advertisement survey system 11 from the contentserver 13.

TABLE 1 Entity Property Data (example) User Geo Location 202.53.15.132Browser Language English Browser Type Internet Explorer 7 OS TypeWindows XP SP2 Screen resolution 1024 × 840 Net Speed 134kbsReferrer-Key word Free money Referrer-Website Google.com Session Length00:02:13 Website Domain (URL) www.freemoney.com Title Free money!Category/Vertical Gambling/Games Web Page URI /Gamblehere.html AdPlacement Size 250 × 250 Relative Position Top Object Type FlashPlacement Group 2 Display Properties float Publisher Name Free MoneyInc. Category/Vertical Gambling/Games Geography USA Network Name ZanoxType Ad-network Redirection URL http://ad.zanox.com/?p=1Category/Vertical Gambling/Games Advertiser Name 888 games Type 888 ltd.Campaign Name Free $50 to spend! Category/Vertical Gambling/GamesLanding URL www.games.888.com/free_m Title Free $50 to spend! Group 5Targeting Plan Day(s) of week Sunday Hour(s) of the day 24h User Freq.Cap Static Geo Targeting USA Language Targeting English Creative TypeFlash Dimensions 250 × 250 File Size 85kb Name 250_250_fg_1.swf

Reference is now made to FIG. 5, which is a simplified flow diagram ofpart 67, according to an exemplary embodiment.

The flow diagram of FIG. 5 is an example of an optimization process bywhich Advertisement survey system 11 optimizes the cell structure of anadvertisement survey, and thereby the processing of the advertisementsurvey. It is appreciated that other means and methods for surveyoptimization are also possible and contemplated.

As shown in FIG. 5, the software module of part 67 starts at step 91 byselecting a first cell. A cell is typically a particular surveyparameter or a particular combination of two or more survey parameters.Part 67 then proceeds to step 92 to select a second cell typically beingadjacent to the first cell. An adjacent cell has the same combination ofsurvey parameters with one (or more) survey parameters having valuesadjacent to the values of the first cell. An adjacent cell may beselected using one or more rules for selecting an adjacent cell asdefined by the user using optimization setup module 42.

For example, an adjacent value may be an adjacent time period, such asadjacent time-of-day period, adjacent day-of-the-week, adjacent agerange, etc. For example, an adjacent value may be an adjacent space suchas adjacent geo-location, adjacent IP address, etc. Adjacent values orvalue range may be network bitrates, screen size, etc. Adjacent valuesmay also refer to qualitative aspects associated with usercharacteristics such as keywords, referral sites, etc.

Cells, or cell parameters can be grouped, for example in a hierarchicalmanner, to provide neighboring (adjacent) values within a group, andadjacent groups. For example, Targeted competitive advertisements can begrouped according to market segments, or further in a hierarchicalsegmentation structure. Similarly, user characteristics can be organizedin groups, and typically in a hierarchical structure.

Organization of survey parameters in hierarchical structure enablesmerging and splitting of parameters according to the hierarchicalstructure. Therefore, parameters such as targeted user characteristicscan be organized in one or more overlapping hierarchical structure,where each such hierarchical structure organizes the same parameters inslightly different groups. This enables splitting parameters (and cells)according to one hierarchical structure and then merging parameters (andcells) according to another hierarchical structure.

Part 67 may then proceed to step 93 to compare the selected cells usingalgorithms, rules and criteria as defined in optimization parameters 43as defined by the user using optimization setup module 42. If acriterion is invoked (step 94), for example, if the comparison (step 93)results in a threshold being surpassed, then part 67 may determine ifthe cells are to be merged (steps 95 and 96), or split (steps 97 and98).

Part 67 may then proceed to step 99 to determine if the optimizationapplies to the current survey or to the next survey and proceedsaccordingly via 75 or 76 of FIG. 3.

If (according to steps 95 and 97) automatic merging and splitting isinappropriate the result may be logged (step 100) for manualoptimization.

The above process may be repeated until the relevant adjacent cells areexhausted (step 101) and until all cells (or cell pairs) pendingoptimization are exhausted (step 102).

In this way advertisement survey system 11 performs a method foroptimizing the advertisements survey by selecting an optimal set ofcells. This optimization process can be also viewed as automaticprofiling, in which the optimal set of user profiles (usercharacteristics) is created. One of the goals of the optimization is toperform the advertisements survey on the smallest number of cells whilepreserving the targeted survey coverage and accuracy.

A different way of describing this method of optimizing advertisementssurveys is by adapting the cell resolution to the variability of theresults. The plurality of cells as defined for a particularadvertisements survey defines the coverage, or space, of theadvertisements survey. The optimization process adapts the size of thecells according to the variability of the results so that in parts ofthe survey space where variability is low the cells are larger, and inparts of the survey space where variability is high the cells aresmaller. Thus a high accuracy is preserved while reducing the number ofcells surveyed. One possible way of measuring the survey accuracy isaccording to the distribution of the variability of the results betweencells.

The method for optimizing a survey of advertisements as described abovemay include the following steps:

A. Receive a plurality of content locations and a plurality of userdata, wherein each pair of content location and user data defines acell.

B. Survey a cell by accessing a content location from an Internet serverand presenting the user data to the content location.

C. Retrieving one or more advertisements from the content locationforming a result for the surveyed cell.

D. Repeat steps b and c for a first plurality of cells;

E. Compare the results of the first plurality of cells forming adistribution of comparisons;

F. Select a second plurality of cells according to one distribution ofsaid comparisons of cells; and

G. Repeat steps b and c for the second plurality of cells.

The second plurality of cells may provide a better accuracy of theadvertisement survey. In this aspect the accuracy may be definedaccording to one range of differences between various survey resultsassociated with adjacent cells. From another aspect, the secondplurality of cells provides a better distribution of the cells of theadvertisement survey. From yet another aspect, the second plurality ofcells provides via the process described above, provides an adaptiveresolution of the cells of the advertisement survey. In this case, thesize, or resolution, of at least part of the cells is adapted accordingto the preferred accuracy of the results.

Adaptive resolution, as described, adapts the cell size according to thedifference in one or more results obtained from adjacent cells. In oneform of adaptation, if the difference is larger than a predeterminedthreshold value, the size of the cell may be reduced (higher resolution)to reduce the difference. If the difference is below a predeterminedthreshold value, the size of the cell may be increased (lowerresolution) to increase the difference. In another form of adaptation,the initial resolution is relatively low, and if the difference issmaller than a predetermined threshold value the size of the cell may bereduced (higher resolution) to trace minute variations of the surveyresults.

As described above, changing the size of a cell may be performed bymerging or splitting one or more adjacent cell dimensions. A celldimension in this respect is a particular characteristic of the cell,such as a characteristic of the virtual user as described above. A celldimension may include a sequence of values, or a sequence of ranges ofvalues, or a sequence of collections of values, of the particularcharacteristic of the cell, or the virtual user. Changing the cell size(or cell resolution) may be performed by merging or splitting adjacentvalues, or ranges of values, or collections of values. The contents of acell dimension (e.g. values, ranges of values, collections of values)may be arranged in a hierarchical structure to simplify the merging andsplitting.

For example, assume an advertisement survey for advertising insport-related websites. To simplify the example the survey cells havethree dimensions: geo-location, sport type, and age group and thecriterion is the number of advertisements in the cell.

Reference is now made to FIG. 6, which is a simplified block diagram ofhierarchical structures of geo-locations, to FIG. 7, which is asimplified block diagram of hierarchical structures of sport types, andto FIG. 8, which is a simplified block diagram of hierarchicalstructures of age ranges, according to an exemplary embodiment.

Following the current example, the survey starts with a coarseresolution using levels 103, 104 and 105 of FIGS. 6, 7 and 8,respectively. There are therefore 24 cells: each having threedimensions:

Geo-location dimension with two options of Spanish-speaking countriesand Brazil.

Sport dimension with two options of ball-games and athletics

Age dimension with six age range options.

The survey results show that the largest difference between the cells isthe number of advertisements for the Spanish speaking countries beingmuch larger than the number of advertisements for Brazil, and that thisdifference is higher than the high-difference threshold. Therefore thegeo-location dimension may be split by selecting the next lower level106 having 5 options (Colombia, Argentina, Peru, Venezuela and therest). The total number of geo-location options is now 6 and the numberof cells is now 72.

The subsequent survey results show that the largest difference betweenthe cells is the number of advertisements for Sport, being much largerfor ball games than athletics and higher than the high-differencethreshold. Therefore the sports dimension is split by selecting the nextlower level 107 having 4 options (athletics, and three groups of ballgames). The total number of cells is now 144.

The subsequent survey results show that the largest difference betweenthe cells along the age dimension is below the low-difference thresholdfor some cell pairs. The age dimension is therefore rotated between theage range options settling on age-range option 108 for soccer, age-rangeoption 109 for golf, and age-range option 110 for the rest of the sportoptions. We now have 6 geo-location options. 6 sport options (athletics,soccer, tennis, golf, squash and the rest of the ball games), and 2age-range options except for soccer and golf having 4 age-range optionseach. Altogether we have 96 cells (6×4×2+6×2×4).

The subsequent survey results show that the difference between tennisand squash is below the low-difference threshold and therefore thesecells are merged, for example by not using virtual users characterizedfor squash. This reduces the total number of cells to 84 cells(6×3×2+6×2×4).

It is expected that during the life of this patent many relevant meansand methods for online (Internet) advertising will be developed and thescope of the terms herein, particularly of the terms “advertisingimpact” and “share of voice”, is intended to include all such newtechnologies a priori. Additional objects, advantages, and novelfeatures of other possible embodiments will become apparent to oneordinarily skilled in the art upon examination of the followingexamples, which are not intended to be limiting. Additionally, each ofthe various embodiments and aspects as delineated hereinabove and asclaimed in the claims section below finds experimental support in thefollowing examples.

It is appreciated that certain features, which are, for clarity,described in the context of separate embodiments, may also be providedin combination in a single embodiment. Conversely, various features,which are, for brevity, described in the context of a single embodiment,may also be provided separately or in any suitable sub-combination.

Although descriptions have been provided above in conjunction withspecific embodiments thereof, it is evident that many alternatives,modifications and variations will be apparent to those skilled in theart. Accordingly, it is intended to embrace all such alternatives,modifications and variations that fall within the spirit and broad scopeof the appended claims. All publications, patents and patentapplications mentioned in this specification are herein incorporated intheir entirety by reference into the specification, to the same extentas if each individual publication, patent or patent application wasspecifically and individually indicated to be incorporated herein byreference. In addition, citation or identification of any reference inthis application shall not be construed as an admission that suchreference is available as prior art.

1.-14. (canceled)
 15. A computer-implemented method of fetching contentover the internet, comprising: receiving a request, the requestcomprising parameters selected by a user of a user device, including anaddress associated with a content server, a geographic locationassociated with a virtual user, and user data associated with thevirtual user; selecting a proxy based at least on the receivedgeographic location parameter; sending a request for the selected proxyto initiate a request to the content server associated with the address,the request to the content server including at least a portion of theuser data; receiving a response that includes at least a portion of aresponse received by the proxy from the content server; and providingthe received response to the user device or enabling the user device toaccess the received response.
 16. The computer-implemented method ofclaim 15, wherein the user data is identified in one or more cookies.17. The computer-implemented method of claim 15, wherein the user datacomprises web browsing history data.
 18. The computer-implemented methodof claim 17, wherein the web browsing history data identifies one ormore web pages or websites that the content server determines thevirtual user may visit before accessing the content.
 19. Thecomputer-implemented method of claim 15, wherein selecting the proxyfurther comprises selecting an IP address of a proxy device from whichto initiate the request to the content server.
 20. Thecomputer-implemented method of claim 15, wherein the response comprisesat least one of: a web page with an advertisement placeholder or anadvertisement associated with the placeholder that is generated based onthe user data.
 21. The computer-implemented method of claim 15, whereinselecting the proxy further comprises selecting a proxy device withinthe same country, city, or town as the received geographic location. 22.The computer-implemented method of claim 15, wherein selecting the proxyfurther comprises selecting a proxy device within the same country,city, or town as the content server.
 23. The computer-implemented methodof claim 15, wherein the address comprises a universal resource locator(URL) and the content server is associated with the URL.
 24. Thecomputer-implemented method of claim 15, wherein the user data comprisespersonal data associated with the virtual user, including at least oneof: a gender, an age or age range, an education level, an income, or asalary.
 25. The computer-implemented method of claim 15, wherein theuser data comprises a session length defining a period of time for theproxy to maintain a session with the content server.
 26. Thecomputer-implemented method of claim 15, wherein the received requestfrom the user device is an HTTP request.
 27. The computer-implementedmethod of claim 15, wherein: the received request from the user devicefurther comprises instructions to repeat selecting a proxy based on oneor more parameters, and requesting the selected proxies to initiaterequests to the content server associated with the address.
 28. Thecomputer-implemented method of claim 27, wherein each of the proxiesselected according to the one or more parameters comprise different IPaddresses.
 29. A computer-implemented method of fetching content overthe internet, comprising: receiving a plurality of requests, eachrequest comprising parameters selected by a user of a user device,including a content address, and a geographic location and user dataassociated with a virtual user; selecting a plurality of proxies basedat least on the received content addresses and received geographiclocations; sending one or more requests for the selected plurality ofproxies to initiate a request to a content server associated with thecontent addresses, each request to the content server including at leasta portion of the user data; receiving one or more responses that includeat least a portion of a response received by the proxies; and providingthe received one or more responses to the user device or enabling theuser device to access the received responses.
 30. Thecomputer-implemented method of claim 29, wherein the responses compriseat least one of: a web page with an advertisement placeholder or anadvertisement associated with the placeholder that is generated based onthe user data.
 31. The computer-implemented method of claim 29, whereinselecting each proxy based on the geographic locations comprisesselecting each proxy within the same country, city, or town as thegeographic locations.
 32. The computer-implemented method of claim 29,wherein selecting each proxy based on the content addresses comprisesselecting each proxy within the same country, city, or town as thecontent server associated with the content addresses.
 33. Thecomputer-implemented method of claim 29, wherein the user data for eachof the one or more virtual users comprises at least one of: internetbrowsing history or personal data associated with the one or more of thevirtual users.
 34. The computer-implemented method of claim 29, whereinthe plurality of requests each include the same content address.